As one of the most important components on the transmission line, the insulator is prone to failure, which affects the safe operation of electrical power system. Hence, it is crucial to accurately detect the insulator defects for further maintenance in time. Recently, with the development of artificial intelligence and target detection algorithms, the insulator defect detection has received more and more attention. However, there are still existing some difficulties: insufficient samples and low detection accuracy. To improve the accuracy of insulator defect detection, this paper proposes an auto-detection method based on an improved lightweight YOLOv5s model. First, this paper introduces the basic network frame of YOLOv5s and proposes an improved algorithm by utilizing the GIoU loss function, Mish activation function, and CBAM module. Then, performs data enhancement in the insulator dataset to enhance the robustness of the model. Finally, trains and tests the improved YOLOv5s model, and compares it with traditional target detection algorithms. Compared with traditional target detection algorithms, the AP value of the proposed algorithm in detecting insulator defects can be improved by 5 %. The results demonstrate the improved algorithm proposed in this paper can effectively identify and position the insulator defects. 

Insulator defect detection in transmission line based on an improved lightweight YOLOv5s algorithm

Ultra-short-term wind speed prediction based on empirical wavelet transform and combined model

UAV formation path planning for mountainous forest terrain utilizing an artificial rabbit optimizer incorporating reinforcement learning and thermal conduction search strategies

Abstract The application of distributed sensors in smart transmission lines to replace traditional inspection methods is an inevitable trend. Currently, the challenge of energy supply for sensors serves as a bottleneck that hinders the intelligent development of transmission lines. This paper focuses on the application of self‐powered inspection technology based on triboelectric and electromagnetic mechanisms in transmission lines. It proposes a self‐powered temperature and vibration monitoring and warning system (STV‐MWS) for multi‐parameter monitoring of transmission line status. This work utilizes the quasi‐zero stiffness structure and center misalignment design to improve the output performance of STV‐MWS at low vibration amplitude, thereby extending its vibration amplitude response range. The STV‐MWS is capable of harvesting and monitoring vibration of 50 µm and above vibration amplitude and 2–700 Hz vibration frequencies, which fully covers the breeze vibration range of transmission lines. Through the split package design, the flexible deployment of STV‐MWS is achieved, further enhancing its engineering application value. This work can effectively ensure that the transmission line inspection can carry out accurate status monitoring and intelligent analysis in the environment characterized by steep terrain, challenging power extraction, and difficult fault judgment, thereby realizing the visualization and intelligence of the transmission line status. 

Multi‐Parameters Self‐Powered Monitoring via Triboelectric and Electromagnetic Mechanisms for Smart Transmission Lines

Fuel cells for multirotor unmanned aerial vehicles: A comparative study of energy storage and performance analysis

Along with the growing size of the power system, the inspection and maintenance of power lines and facilities in complicated environments will be put to a greater test. Currently, more and more research are being done on UAV-based transmission line inspection techniques, of which multi-UAV task assignment and path planning are two important aspects. This paper first generates a three-dimensional inspection environment model while considering and measuring the influence of numerous wind fields in the mountain environment. Next, a multi-indicator hybrid cost function that considers wind field influence is defined for the multi-UAV task assignment problem. An improved Bidirectional Ant Colony and discrete Honey Badger Algorithm (BACOHBA) is proposed to solve it. Then, the multi-UAV path planning problem based on the multi-wind field condition is defined, and an improved Honey Badger-Fruit Fly Algorithm (HBAFOA) is proposed to solve it. Finally, simulation experiments are conducted under different algorithms, which prove that the proposed algorithm has better performance in terms of fast-solving ability, solution accuracy, and optimization stability. 

Multi-mechanism swarm optimization for multi-UAV task assignment and path planning in transmission line inspection under multi-wind field

A High-performance Small Target Defect Detection Method for PCB Boards Based on a Novel YOLO-DFA Algorithm

Optimized Extreme Learning Machine by an Improved Harris Hawks Optimization Algorithm for Mine Fire Flame Recognition

Nonlinearity and nonstationary data affect the prediction accuracy of network traffic. A practical solution is to use an integrated modeling method based on time‐series decomposition and an extreme learning machine (ELM). The original network traffic data series are decomposed in this paper using variational mode decomposition (VMD), and then each subdata series is subjected to phase space reconstruction (PSR). Finally, ELM trains a model for predicting network traffic. We used scalable artificial bee colony (SABC) algorithms to optimize parameter selection throughout the modeling process. However, several processes, such as VMD decomposition, PSR, and ELM training, will be repeated during iterative model optimization, resulting in a significant increase in computational complexity. This article proposes an adaptive selection mechanism for decomposing time series, which named is SABC‐VMD‐ELM prediction method with an adaptive selection (SAVE‐AS) method. From gray correlation degree and approximate entropy, an adaptive selection operator is constructed that can adaptively select the number of data sets after VMD optimization decomposition. The ELM model is trained using several subdata sequences that satisfy the conditions rather than all subdata sequences to minimize computational complexity in modeling. Two publicly available data sets of Mackey–Glass and Lorenz chaotic time series were used; in addition, actual network traffic data from the MAWI Working Group's WIDE Backbone was simulated. It is demonstrated that the prediction model can reduce computational complexity and accelerate convergence speed while maintaining the model's predictive accuracy. In three data sets, training time was reduced by 25.25%, 23.87%, and 41.36%, respectively.

SAVE‐AS: Accelerating convergence in network traffic prediction through adaptive optimized variational mode decomposition and an integrated extreme learning machine

A PCB bare board defects detection method based on dual-domain wavelet-YOLO

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